157 lines
4.8 KiB
Python
157 lines
4.8 KiB
Python
#! /usr/bin/env python3
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, AdamW
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from torch.optim.lr_scheduler import ReduceLROnPlateau
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import torch
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from dl import load_dataset
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from tqdm import tqdm
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import os
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class FT:
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def __init__(self):
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torch.backends.cudnn.benchmark = True
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self.model_name = "facebook/mbart-large-50"
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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# load tokenizer and model
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self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
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self.model = AutoModelForSeq2SeqLM.from_pretrained(
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self.model_name, load_in_8bit=True
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)
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# set up optimizer
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self.optimizer = torch.optim.AdamW(self.model.parameters(), lr=1e-5)
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self.scheduler = ReduceLROnPlateau(
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self.optimizer, mode="min", patience=1, factor=0.5
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) # add this line
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try:
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from torch.cuda.amp import GradScaler, autocast
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self.scaler = GradScaler()
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except ImportError:
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class autocast:
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def __enter__(self):
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pass
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def __exit__(self, *args):
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pass
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self.scaler = None # We won't use a scaler if we don't have Amp
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def train_model(self, dataloader):
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self.model.train()
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total_loss = 0
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print("Training model...")
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for batch in tqdm(dataloader):
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self.optimizer.zero_grad()
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inputs = self.tokenizer(
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batch[1],
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return_tensors="pt",
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padding=True,
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truncation=True,
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max_length=512,
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)
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inputs.to(self.device)
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labels = self.tokenizer(
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batch[0],
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return_tensors="pt",
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padding=True,
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truncation=True,
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max_length=512,
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)
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labels.to(self.device)
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outputs = self.model(
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input_ids=inputs["input_ids"],
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attention_mask=inputs["attention_mask"],
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labels=labels["input_ids"],
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)
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loss = outputs.loss
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loss.backward()
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self.optimizer.step()
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total_loss += loss.item()
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avg_train_loss = total_loss / len(dataloader)
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return avg_train_loss
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def test_model(self, dataloader):
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self.model.eval()
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total_loss = 0
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print("Testing model...")
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for batch in tqdm(dataloader):
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with torch.no_grad():
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inputs = self.tokenizer(
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batch[1],
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return_tensors="pt",
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padding=True,
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truncation=True,
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max_length=512,
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)
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inputs.to(self.device)
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labels = self.tokenizer(
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batch[0],
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return_tensors="pt",
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padding=True,
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truncation=True,
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max_length=512,
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)
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labels.to(self.device)
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outputs = self.model(
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input_ids=inputs["input_ids"],
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attention_mask=inputs["attention_mask"],
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labels=labels["input_ids"],
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)
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loss = outputs.loss
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total_loss += loss.item()
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avg_test_loss = total_loss / len(dataloader)
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return avg_test_loss
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def save_checkpoint(self, epoch):
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os.makedirs("./checkpoints/ft", exist_ok=True)
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torch.save(
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{
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"epoch": epoch,
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"model_state_dict": self.model.state_dict(),
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"optimizer_state_dict": self.optimizer.state_dict(),
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},
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f"./checkpoints/ft/ft_{epoch}.pt",
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)
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def train(self):
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train_dataloader, test_dataloader = load_dataset(
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"./dataset/hason_out.json",
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200,
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200,
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1,
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test_ratio=0.2,
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)
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num_epochs = 100
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last_lr = None
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for epoch in range(num_epochs):
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avg_train_loss = self.train_model(train_dataloader)
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print(f"Train loss for epoch {epoch+1}: {avg_train_loss}")
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self.save_checkpoint(epoch)
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print("Checkpoint saved!")
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avg_test_loss = self.test_model(test_dataloader)
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print(f"Test loss for epoch {epoch+1}: {avg_test_loss}")
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# Check if the learning rate has changed
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current_lr = self.optimizer.param_groups[0]["lr"]
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if last_lr and current_lr != last_lr:
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print(f"Learning rate reduced from {last_lr} to {current_lr}")
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last_lr = current_lr
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if __name__ == "__main__":
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trainer = FT()
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trainer.train()
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